ArticleStructure (London, England : 1993)2024
Accurate modeling of peptide-MHC structures with AlphaFold.
Article in Structure (London, England : 1993), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An In Silico Approach.Probiotics and antimicrobial proteins · 2025Pooled it
- Multi-dimensional orchestration of binders for improved CAR-T immunotherapy.Cancer letters · 2026Review
- T Cell Thoughts.Immunological reviews · 2026Review
- Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.Briefings in bioinformatics · 2026Article
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3.bioRxiv : the preprint server for biology · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- A high-speed attention network for MHC-bound peptide identification and 3D modeling.Cell reports methods · 2026Article
- Dynamic Allostery in T Cell Receptor Specificity: A Role for Peptides and MHC Polymorphisms in Allosterically Tuning Immune Recognition.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- Dynamical network analysis reveals long-range residue couplings at the pMHC interface underlying enhanced immunogenicity.NPJ systems biology and applications · 2026Article
- Multi-strategy embedded framework for neoantigen vaccine maturation.Scientific reports · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- STRUMP-I: Structure-Based Machine Learning Approach to pMHC-I Binding Prediction Using Force Field Energy Features.Computational and structural biotechnology journal · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Predicted peptide scaffolds for drug screening in endometrial cancer organoids.Scientific reports · 2025Article
- Structural changes from wild-type define tumor-rejecting neoantigens.Journal for immunotherapy of cancer · 2025Article
- Advancing human leukocyte antigen-based cancer immunotherapy: from personalized to broad-spectrum strategies for genetically heterogeneous populations.Trends in cancer · 2025Review
- Structural Study of a Peptide Epitope Bearing Multiple Post-Translational Modifications in Rheumatoid Arthritis.International journal of molecular sciences · 2025Article
- STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.